AICodexLab/answerdotai-ModernBERT-base-ai-detector

answerdotai-ModernBERT-base-ai-detector

This model is a fine-tuned version of answerdotai/ModernBERT-base on the AI vs Human Text Classification dataset, DAIGT V2 Train Dataset.

It achieves the following results on the evaluation set:

• —Validation Loss: 0.0036

📝 Model Description

This model is based on ModernBERT-base, a lightweight and efficient BERT-based model. It has been fine-tuned for AI-generated vs Human-written text classification, allowing it to distinguish between texts written by AI models (ChatGPT, DeepSeek, Claude, etc.) and human authors.

🎯 Intended Uses & Limitations

✅ Intended Uses

• —AI-generated content detection (e.g., ChatGPT, Claude, DeepSeek).
• —Text classification for distinguishing human vs AI-generated content.
• —Educational & Research applications for AI-content detection.

⚠️ Limitations

• —Not 100% accurate → Some AI texts may resemble human writing and vice versa.
• —Limited to trained dataset scope → May struggle with out-of-domain text.
• —Bias risks → If the dataset contains bias, the model may inherit it.

📊 Training and Evaluation Data

• —The model was fine-tuned on 35,894 training samples and 8,974 test samples.
• —The dataset consists of AI-generated text samples (ChatGPT, Claude, DeepSeek, etc.) and human-written samples (Wikipedia, books, articles).
• —Labels:
• —1 → AI-generated text
• —0 → Human-written text

⚙️ Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

📈 Training Results

🛠 Framework Versions

📤 Model Usage

To load and use the model for text classification:

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_name = "answerdotai/ModernBERT-base-ai-detector"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Create text classification pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Run classification
text = "This text was written by an AI model like ChatGPT."
result = classifier(text)

print(result)